经验模态分解结合频谱质心的方法在油管入侵信号诊断中的应用

发布时间：2018-02-09 14:27

本文关键词： 经验模态分解(EMD) 频谱质心奇异值分解(SVD) 安全防范系统信号重构频谱分析　出处：《光电子·激光》2017年08期 　论文类型：期刊论文

【摘要】：在输油管道的安全防范系统应用背景下,针对传统方法诊断光纤采集到的入侵信号准确率不高的问题,提出一种基于经验模态分解(EMD)算法和频谱质心(SC)的入侵信号诊断方法。首先将采集到的原始入侵信号通过EMD进行分解,分离含噪最多的特征模态函数(IMF)分量,再组合剩余的IMF分量形成重构信号,对重构信号进行希尔伯特变换(HT)得到希尔伯特谱,计算它的SC,进一步识别入侵信号和干扰信号。通过对油管振动信号进行实验,本文方法对于每种入侵信号和干扰信号的诊断准确率均在90.00%以上,整体的诊断准确率达到97.17%。对于该组油管振动信号,同时运用奇异值分解(SVD)法进行诊断并将其结果与本文方法的诊断结果进行对比,整体上本文方法的诊断准确率比SVD法高出19.00%。仿真实验结果表明,本文方法能有效诊断入侵信号,并且诊断效果明显优于奇异值分解法。
[Abstract]:Under the background of the application of oil pipeline security prevention system, aiming at the problem that the traditional method of diagnosing the intrusion signal acquired by optical fiber is not accurate enough, An intrusion signal diagnosis method based on empirical mode decomposition (EMD) algorithm and spectral centroid (SCC) is proposed. Firstly, the original intrusion signal is decomposed by EMD to separate the most noise-containing characteristic mode function (IMF) component. The reconstructed signal is formed by combining the remaining IMF components, and the Hilbert spectrum is obtained by Hilbert transform. The Hilbert spectrum is calculated, and the intrusion signal and the interference signal are further recognized. The experiment is carried out on the vibration signal of the tubing. The diagnostic accuracy of this method for each intrusion signal and interference signal is more than 90.00%, and the overall diagnostic accuracy is 97.17. At the same time, the singular value decomposition (SVD) method is used to diagnose and compare the results with the results of this method. The diagnostic accuracy of this method is 19.00 higher than that of SVD method. The simulation results show that the proposed method can effectively diagnose the intrusion signal. And the diagnostic effect is obviously better than the singular value decomposition method.
【作者单位】：浙江理工大学机械与自动控制学院;
【基金】：国家自然科学基金(61503341)资助项目
【分类号】：TE973

【相似文献】